Home|Nintex Blog|AI, automation and the future of banking

AI, automation and the future of banking

In a recent BankDirector article, Dallas Wells of PrecisionLender explains that banks have spent many years and many millions of dollars on ways to better measure, monitor and price risk, but eventually, the final decision on every commercial banking deal comes down to human beings. Artificial Intelligence or AI can aid in that important process and banking automation software provides risk assessment advantages in economy, ease of use and effectiveness.

When evaluating commercial banking opportunities for risk, banks and credit unions must first achieve consistency. It shouldn’t be stubborn, old-school pros competing with young analysts, who perhaps haven’t experienced a true market correction, to guide the risk management process. As Wells puts it, “flying by the seat of your pants” is no way to go.

AI is becoming increasingly mainstream with assistance from software such as Siri, Alexa, Cortana, etc.

The banking industry is moving forward by evaluating assistants like this and implementing adaptive technology such as automation tools to move banking forward.

Gartner, Inc. reports that banking automation tools, like Nintex RPA, have evolved with greater logic and applicability over the last 20 years, requiring only a three to four-month implementation time, offering a three to six-month ROI and ranging in cost from $1,500 to $9,000. In fact, these software tools work in conjunction with a bank’s commercial banking staff to help streamline many processes.

While commercial banking specialists are working the frontlines to monitor and assess risk, web automation software facilitates the heightened harvesting of actionable data from project files, websites and online databases. Through web scraping, data mining and other information extraction, vital information for a bank’s planning and risk management efforts are secured and reported.

Commercial banking specialists can create a reusable if/then script that reads and reacts to many search criteria. Any number of data variables can be factored into the retrieval process so that banks can pull deal metrics, case studies and other data to help them make the most informed decision on the opportunity — and the accompanying credit risk — at hand.

More evolved and affordable than AI platforms, banking automation software delivers speed and accuracy — and thus consistency — so that financial models and algorithms have the right numbers to crunch. Then commercial banking leaders can use the data from past performance and current patterns and correlations to plot the right path into their financial future.



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